This research investigates whether large language models can effectively evaluate code and text in software engineering tasks, potentially replacing human reviewers.

• Strong correlation between LLM and human evaluations across multiple SE tasks
• Competitive performance of LLMs against traditional metrics like BLEU and Pass@k
• Cost-effective alternative to human evaluation with 95% reduction in evaluation time
• Clear evaluation protocols with few-shot prompting improve LLM judgment reliability

For engineering teams, this research offers a practical way to automate code quality assessment, reducing manual review time while maintaining evaluation accuracy. The findings suggest LLMs can serve as reliable first-pass evaluators in software development workflows.

Original Paper: Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering